The computer will be able to diagnose skin cancer more accurately doctors

An worldwide team of researchers say they have developed an artificial intelligence system that diagnoses skin cancer more accurately than trained dermatologists. The computer is made up of an artificial network of nerves that mimic the processes of the brain as it computes information taken in from the eyes.

Researchers from France and Germany trained the CNN by showing it more than 100,000 photos of malignant melanomas as well as and benign moles and lesions.

Fifty eight dermatologists from 17 countries around the world participated in the study.

The number of skin cancer cases missed by the CNN were fewer than those gone unnoticed by dermatologists, indicating a higher sensitivity.

The dermatologists' performance improved when they were given more information about the patients and their skin lesions. The computer was also less likely to misdiagnose a Benign Mole as Malignant. The American Cancer Society estimates 9,320 people will die from melanoma in 2018 while 91,270 new cases will be diagnosed. The study results explained that an AI system was more proficient than human dermatologists at detecting the cancer cells. Doctors correctly found skin cancer and benign moles 86.6 percent of the time, but CNN was correct 95 percent of the time. Haenssle said that the findings show that "deep learning convolutional neural networks are capable of out-performing dermatologists, including extensively trained experts, in the task of detecting melanomas".

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Malignant melanoma accounts for 1 percent of all skin cancers but causes a majority of skin cancer-related deaths. About 19% had their years of experience ranging from two to five years while the remaining 29% were new doctors with less than two years of working experience in the field. The cancer can be cured, but it typically requires an early diagnosis.

In some parts of the body - fingers, toes or scalp, melanoma is hard to image, and AI can not easily recognize atypical lesions of the skin.

"Currently, there is no substitute for a thorough clinical examination", experts Victoria Mar from Monash University in Melbourne and Peter Soyer of the University of Queensland wrote in an editorial published with the study. "Still, there is much more work to be done to implement this exciting technology safely into routine clinical care".

Some 100,000 images were needed to get the AI's deep learning convolutional neural network (CNN) up-to-scratch.

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